Smart ArM: a customizable and versatile robotic arm prosthesis platform for Cybathlon and research.

BACKGROUND: In the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants. METHODS: To meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics. RESULTS: The Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity. CONCLUSION: These application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.

Simultaneous Control of 2DOF Upper-Limb Prosthesis With Body Compensations-Based Control: A Multiple Cases Study.

Controlling several joints simultaneously is a common feature of natural arm movements. Robotic prostheses shall offer this possibility to their wearer. Yet, existing approaches to control a robotic upper-limb prosthesis from myoelectric interfaces do not satisfactorily respond to this need: standard methods provide sequential joint-by-joint motion control only; advanced pattern recognition-based approaches allow the control of a limited subset of synchronized multi-joint movements and remain complex to set up. In this paper, we exploit a control method of an upper-limb prosthesis based on body motion measurement called Compensations Cancellation Control (CCC). It offers a straightforward simultaneous control of the intermediate joints, namely the wrist and the elbow. Four transhumeral amputated participants performed the Refined Rolyan Clothespin Test with an experimental prosthesis alternatively running CCC and conventional joint-by-joint myoelectric control. Task performance, joint motions, body compensations and cognitive load were assessed. This experiment shows that CCC restores simultaneity between prosthetic joints while maintaining the level of performance of conventional myoelectric control (used on a daily basis by three participants), without increasing compensatory motions nor cognitive load.

The stability investigation of variable viscosity control in the human-robot interaction.

BACKGROUND: For many co-manipulative applications, variable damping is a valuable feature provided by robots. One approach is implementing a high viscosity at low velocities and a low viscosity at high velocities. This, however, is proven to have the possibility to alter human natural motion performance. METHODS: We show that the distortion is caused by the viscosity drop resulting in robot's resistance to motion. To address this, a method for stably achieving the desired behaviour is presented. It involves leveraging a first-order linear filter to slow the viscosity variation down. RESULTS: The proposition is supported by a theoretical analysis using a robotic model. Meanwhile, the user performance in human-robot experiments gets significantly improved, showing the practical efficiency in real applications. CONCLUSIONS: This paper discusses the variable viscosity control in the context of co-manipulation. An instability problem and its solution were theoretically shown and experimentally evidenced through human-robot experiments.

Design of a robotic zebra finch for experimental studies on developmental song learning.

Birdsong learning has been consolidated as the model system of choice for exploring the biological substrates of vocal learning. In the zebra finch (Taeniopygia guttata), only males sing and they develop their song during a sensitive period in early life. Different experimental procedures have been used in the laboratory to train a young finch to learn a song. So far, the best method to achieve a faithful imitation is to keep a young bird singly with an adult male. Here, we present the different characteristics of a robotic zebra finch that was developed with the goal to be used as a song tutor. The robot is morphologically similar to a life-sized finch: it can produce movements and sounds contingently to the behaviours of a live bird. We present preliminary results on song imitation, and other possible applications beyond the scope of developmental song learning.

Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study.

Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most upper-limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

Voluntary phantom hand and finger movements in transhumerai amputees could be used to naturally control polydigital prostheses.

An arm amputation is extremely invalidating since many of our daily tasks require bi-manual and precise control of hand movements. Perfect hand prostheses should therefore offer a natural, intuitive and cognitively simple control over their numerous biomimetic active degrees of freedom. While efficient polydigital prostheses are commercially available, their control remains complex to master and offers limited possibilities, especially for high amputation levels. In this pilot study, we demonstrate the possibility for upper-arm amputees to intuitively control a polydigital hand prosthesis by using surface myoelectric activities of residual limb muscles (sEMG) associated with phantom limb movements, even if these residual arm muscles on which the phantom activity is measured were not naturally associated with hand movements before amputation. Using pattern recognition methods, three arm amputees were able, without training, to initiate 5-8 movements of a robotic hand (including individual finger movements) by simply mobilizing their phantom limb while the robotic hand was mimicking the action in real time. This innovative control approach could offer to numerous upper-limb amputees an access to recent biomimetic prostheses with multiple controllable joints, without requiring surgery or complex training; and might deeply change the way the phantom limb is apprehended by both patients and clinicians.

Classification of Phantom Finger, Hand, Wrist, and Elbow Voluntary Gestures in Transhumeral Amputees With sEMG.

Decoding finger and hand movements from sEMG electrodes placed on the forearm of transradial amputees has been commonly studied by many research groups. A few recent studies have shown an interesting phenomenon: simple correlations between distal phantom finger, hand and wrist voluntary movements and muscle activity in the residual upper arm in transhumeral amputees, i.e., of muscle groups that, prior to amputation, had no physical effect on the concerned hand and wrist joints. In this study, we are going further into the exploration of this phenomenon by setting up an evaluation study of phantom finger, hand, wrist and elbow (if present) movement classification based on the analysis of surface electromyographic (sEMG) signals measured by multiple electrodes placed on the residual upper arm of five transhumeral amputees with a controllable phantom limb who did not undergo any reinnervation surgery. We showed that with a state-of-the-art classification architecture, it is possible to correctly classify phantom limb activity (up to 14 movements) with a rather important average success (over 80% if considering basic sets of six hand, wrist and elbow movements) and to use this pattern recognition output to give online control of a device (here a graphical interface) to these transhumeral amputees. Beyond changing the way the phantom limb condition is apprehended by both patients and clinicians, such results could pave the road towards a new control approach for transhumeral amputated patients with a voluntary controllable phantom limb. This could ease and extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.